白:一个传统的中医大语言模型
IEEE journal of biomedical and health informatics
|September 24, 2025
概括
本研究介绍了BianCang,这是一个专门用于传统中医药 (TCM) 的大型语言模型 (LLM). 通过整合特定领域的知识和有针对性的培训,BianCang增强了TCM诊断和综合症差异化.
科学领域:
- 人工智能在医学中的应用
- 传统中国医学 (TCM) 是一种
背景情况:
- 大型语言模型 (LLM) 在医学上表现有前途,但由于理论差异和数据稀缺,与TCM斗争.
- 现有的医学LLM缺乏专门的机构,用于TCM诊断和综合症分化.
研究的目的:
- 开发一个特定于TCM的LLM,BianCang,以解决TCM应用中的当前模型的局限性.
- 提高在TCM的背景下LLMs的诊断和区分能力.
主要方法:
- 采用了两阶段的培训过程,涉及到特定领域的知识注入和有针对性的调整.
- 利用预训练集体,来自医院记录的与指令一致的数据集和ChP-TCM数据集.
- 编制了广泛的TCM和医疗机构,用于持续的预培训和监督的微调.
主要成果:
- 在11个测试组,31个模型和4个任务中进行的评估证明了BianCang的有效性.
- 在TCM诊断和综合征区分任务中,BianCang显示出显著的改进.
- 开发的模型为推进TCM中的AI提供了有价值的见解.
结论:
- 白代表了在将LLMs应用于传统中医学的重大进步.
- 拟议的培训方法和数据集提高了专业医学领域的LLM绩效.
- 代码,数据集和模型的可用性促进了人工智能驱动的TCM的未来研究和开发.
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